Fixes citation by rebuilding readme

README.Rmd

@@ -15,9 +15,9 @@ knitr::opts_chunk$set(
 
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+[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/epiforecasts/covidregionaldata/blob/master/LICENSE.md/)   [![GitHub contributors](https://img.shields.io/github/contributors/epiforecasts/covidregionaldata)](https://github.com/epiforecasts/covidregionaldata/graphs/contributors)  [![Discord](https://img.shields.io/discord/864828485981306890?logo=Discord)](https://discord.gg/9YPDDADVt3)  [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-yellow.svg)](https://makeapullrequest.com/) [![GitHub commits](https://img.shields.io/github/commits-since/epiforecasts/covidregionaldata/0.9.2.svg?color=orange)](https://github.com/epiforecasts/covidregionaldata/commit/master/) 
 
-[![DOI](https://joss.theoj.org/papers/10.21105/joss.03290/status.svg)](https://doi.org/10.21105/joss.03290)
+[![JOSS](https://joss.theoj.org/papers/10.21105/joss.03290/status.svg)](https://doi.org/10.21105/joss.03290) [![Zenodo](https://zenodo.org/badge/271601189.svg)](https://zenodo.org/badge/latestdoi/271601189)
 
 Interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. This package is designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. This allows users to inspect and scrutinise the data, and tools used to process it, at every step. For all countries supported, data includes a daily time-series of cases and, wherever available, data on deaths, hospitalisations, and tests. National level data is also supported using a range of data sources as well as line list data and links to intervention data sets.
 

README.md

@@ -18,9 +18,9 @@ contributors](https://img.shields.io/github/contributors/epiforecasts/covidregio
 Welcome](https://img.shields.io/badge/PRs-welcome-yellow.svg)](https://makeapullrequest.com/)
 [![GitHub
 commits](https://img.shields.io/github/commits-since/epiforecasts/covidregionaldata/0.9.2.svg?color=orange)](https://github.com/epiforecasts/covidregionaldata/commit/master/)
-[![DOI](https://zenodo.org/badge/271601189.svg)](https://zenodo.org/badge/latestdoi/271601189)
 
-[![DOI](https://joss.theoj.org/papers/10.21105/joss.03290/status.svg)](https://doi.org/10.21105/joss.03290)
+[![JOSS](https://joss.theoj.org/papers/10.21105/joss.03290/status.svg)](https://doi.org/10.21105/joss.03290)
+[![Zenodo](https://zenodo.org/badge/271601189.svg)](https://zenodo.org/badge/latestdoi/271601189)
 
 Interface to subnational and national level COVID-19 data sourced from
 both official sources, such as Public Health England in the UK, and from
@@ -80,7 +80,7 @@ the temporary directory by default),
 
 ``` r
 start_using_memoise()
-#> Using a cache at: /tmp/RtmpmGP3Do
+#> Using a cache at: /tmp/RtmprTOAdV
 ```
 
 To stop using `memoise` use,
@@ -105,19 +105,19 @@ the Google COVID-19 open data project), use:
 ``` r
 nots <- get_national_data()
 #> Downloading data from https://covid19.who.int/WHO-COVID-19-global-data.csv
-#> Rows: 132,360
-#> Columns: 8
+#> Rows: 132483 Columns: 8
+#> ── Column specification ────────────────────────────────────────────────────────
 #> Delimiter: ","
-#> chr  [3]: Country_code, Country, WHO_region
-#> dbl  [4]: New_cases, Cumulative_cases, New_deaths, Cumulative_deaths
-#> date [1]: Date_reported
+#> chr  (3): Country_code, Country, WHO_region
+#> dbl  (4): New_cases, Cumulative_cases, New_deaths, Cumulative_deaths
+#> date (1): Date_reported
 #> 
-#> Use `spec()` to retrieve the guessed column specification
-#> Pass a specification to the `col_types` argument to quiet this message
+#> ℹ Use `spec()` to retrieve the full column specification for this data.
+#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
 #> Cleaning data
 #> Processing data
 nots
-#> # A tibble: 132,482 x 15
+#> # A tibble: 132,483 x 15
 #>    date       un_region who_region country        iso_code cases_new cases_total
 #>    <date>     <chr>     <chr>      <chr>          <chr>        <dbl>       <dbl>
 #>  1 2020-01-03 Asia      EMRO       Afghanistan    AF               0           0
@@ -130,7 +130,7 @@ nots
 #>  8 2020-01-03 Americas  AMRO       Antigua & Bar… AG               0           0
 #>  9 2020-01-03 Americas  AMRO       Argentina      AR               0           0
 #> 10 2020-01-03 Asia      EURO       Armenia        AM               0           0
-#> # … with 132,472 more rows, and 8 more variables: deaths_new <dbl>,
+#> # … with 132,473 more rows, and 8 more variables: deaths_new <dbl>,
 #> #   deaths_total <dbl>, recovered_new <dbl>, recovered_total <dbl>,
 #> #   hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>
 ```
@@ -171,7 +171,7 @@ for example by level 1 region in the UK, use:
 ``` r
 uk_nots <- get_regional_data(country = "UK", verbose = FALSE)
 uk_nots
-#> # A tibble: 6,903 x 26
+#> # A tibble: 6,916 x 26
 #>    date       region   region_code cases_new cases_total deaths_new deaths_total
 #>    <date>     <chr>    <chr>           <dbl>       <dbl>      <dbl>        <dbl>
 #>  1 2020-01-30 East Mi… E12000004          NA          NA         NA           NA
@@ -184,7 +184,7 @@ uk_nots
 #>  8 2020-01-30 Scotland S92000003          NA          NA         NA           NA
 #>  9 2020-01-30 South E… E12000008          NA          NA         NA           NA
 #> 10 2020-01-30 South W… E12000009          NA          NA         NA           NA
-#> # … with 6,893 more rows, and 19 more variables: recovered_new <dbl>,
+#> # … with 6,906 more rows, and 19 more variables: recovered_new <dbl>,
 #> #   recovered_total <dbl>, hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>,
 #> #   tested_total <dbl>, areaType <chr>, cumCasesByPublishDate <dbl>,
 #> #   cumCasesBySpecimenDate <dbl>, newCasesByPublishDate <dbl>,
@@ -239,7 +239,7 @@ using the following,
     #>   Joseph Palmer, Katharine Sherratt, Richard Martin-Nielsen, Jonnie
     #>   Bevan, Hamish Gibbs, Sebastian Funk and Sam Abbott (2021).
     #>   covidregionaldata: Subnational data for COVID-19 epidemiology, DOI:
-    #>   10.5281/zenodo.3957539
+    #>   10.21105/joss.03290
     #> 
     #> A BibTeX entry for LaTeX users is
     #> 
@@ -249,9 +249,9 @@ using the following,
     #>     journal = {Journal of Open Source Software},
     #>     year = {2021},
     #>     volume = {6},
-    #>     number = {62},
+    #>     number = {63},
     #>     pages = {3290},
-    #>     doi = {10.5281/zenodo.3957539},
+    #>     doi = {10.21105/joss.03290},
     #>   }
 
 ## Development
@@ -265,7 +265,7 @@ would like to thank the [CMMID COVID-19 working
 group](https://cmmid.github.io/groups/ncov-group.html) for insightful
 comments and feedback.
 
-We welcome contributions and new contributors\! We particularly
+We welcome contributions and new contributors! We particularly
 appreciate help adding new data sources for countries at sub-national
 level, or work on priority problems in the
 [issues](https://github.com/epiforecasts/covidregionaldata/issues).